Geography information
Geography information-preprocess:kmean
hc<-hclust(dist(crowdfunding),method = "ward.D", members = NULL)
NAs introduced by coercion
plclust(hc)
'plclust' is deprecated.
Use 'plot' instead.
See help("Deprecated")
rect.hclust(hc,k=4)

heatmap(as.matrix(dist(crowdfunding,method= 'euclidean')),labRow = F, labCol = F)
NAs introduced by coercion

result<-cutree(hc,k=4)
result.category<-as.data.frame(result)
colnames(result.category)<-c("MDS_Category")
result.category
pie(result)

barplot(result,col =result )

#table(result)
#summary(result)
plot(result,type = "p",col=result,xlab="State",xaxt="n",ylab="MDS_Category")

library(ggplot2)
mds2 <- -cmdscale(dist(crowdfunding))
NAs introduced by coercion
plot(mds2, type="n", axes=FALSE, ann=FALSE)
text(mds2, labels=rownames(mds2), xpd = NA)

mds<-cmdscale(dist(crowdfunding),k=4,eig=T)
NAs introduced by coercion
x = mds$points[,1]
y = mds$points[,2]
p=ggplot(data.frame(x,y),aes(x,y))
p+geom_point(size=5 , alpha=0.8 , aes(colour=factor(result) ))

k2<-kmeans(all,centers=5,nstart=10)
summary(k2)
Length Class Mode
cluster 49 -none- numeric
centers 225 -none- numeric
totss 1 -none- numeric
withinss 5 -none- numeric
tot.withinss 1 -none- numeric
betweenss 1 -none- numeric
size 5 -none- numeric
iter 1 -none- numeric
ifault 1 -none- numeric
Geography Information-simpleplot
par(mfrow=c(1,2) )
#count_of_Grand.Total
plot(crowdfunding$count_of_Grand.Total,col=crowdfunding$Region, main="Count of Project Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("center",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
#successful.rate
plot(crowdfunding$successful.rate,col=crowdfunding$Region, main="Successful Rate Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("bottomleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
Factors analysis
This article is to analyse the factors to the crowdfunding successful rate. I guess the Education, the inequity of family income and the poverty rate may be related to the crowdfunding successful rate. and in the follow context, i will analyse the those factors.
Firstly, The Statistical Summary ### Factors Analysis-Statistical Summary
library(moments)
summary(crowdfunding$successful.rate)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1250 0.3179 0.3636 0.3631 0.4095 0.5484
kurtosis(crowdfunding$successful.rate)
[1] 3.630147
summary(crowdfunding$GiniCoeff)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4190 0.4400 0.4530 0.4522 0.4658 0.4990
kurtosis(crowdfunding$GiniCoeff)
[1] 2.552647
summary(crowdfunding$pAdDeg)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.06100 0.07950 0.09200 0.09794 0.11000 0.16400
kurtosis(crowdfunding$pAdDeg)
[1] 3.382781
summary(crowdfunding$PovRate1)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0920 0.1212 0.1480 0.1480 0.1705 0.2190
kurtosis(crowdfunding$PovRate1)
[1] 2.159154
Factors Analysis-Plot for Factors
boxplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,crowdfunding$pAdDeg,crowdfunding$PovRate1,names = c("Successful Rate","GiniCoeff","Higher Education","PovRate1"),main="Factors Box Plot")
par(mfrow=c(1,3))

plot(crowdfunding$successful.rate,col="red",pch=10,xlab="State",ylab="Successful Rate",xaxt="n",main="Successful Rate Plot")
plot(crowdfunding$GiniCoeff,col="green",pch=18,xlab="State",xaxt="n",ylab="GiniCoeff ",xaxt="n",main="GiniCoeff Plot")
plot(crowdfunding$pAdDeg,col="blue",pch=15,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot")

#plot(crowdfunding$PovRate1,col="black",pch=16,xlab="State",xaxt="n",ylab="Poverty Rate",xaxt="n",main="Poverty Rate Plot")
library(car)
scatterplot(crowdfunding$successful.rate,log(crowdfunding$average_of_goal_Grand.Total),pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$GiniCoeff,pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$PovRate1,pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$Densitym2,pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$pHigh,pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$pBatDeg,pch=19)

scatterplot(crowdfunding$successful.rate~crowdfunding$pAdDeg,pch=19)

Factors Analysis-Successful Rate|PovRate1
#redo scatterplot with Successful Rate-PovRate1
scatterplot(crowdfunding$successful.rate,crowdfunding$PovRate1,pch=19)

anova(successful.rate2PovRate1)
Analysis of Variance Table
Response: crowdfunding$successful.rate
Df Sum Sq Mean Sq F value Pr(>F)
crowdfunding$PovRate1 1 0.01157 0.0115683 1.4698 0.2313
Residuals 48 0.37780 0.0078708
ggplot(crowdfunding,aes(x=PovRate1,y=successful.rate,main = "Successful rate~PovRate"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")
par(mfrow=c(1,2))

boxplot(crowdfunding$successful.rate,crowdfunding$PovRate1,names=c("Successful Rate","PovRate1"))
boxplot(crowdfunding$successful.rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful.rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)],col = c("green","deepskyblue"),names=c("Successful%(High PovRate)","Successful%(Low PovRate)"),xlab="Successful rate by PovRate1")

t.test(crowdfunding$successful.rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful.rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)])
Welch Two Sample t-test
data: crowdfunding$successful.rate[crowdfunding$PovRate1 > mean(crowdfunding$PovRate1)] and crowdfunding$successful.rate[crowdfunding$PovRate1 <= mean(crowdfunding$PovRate1)]
t = -0.01904, df = 43.704, p-value = 0.9849
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.05105839 0.05010288
sample estimates:
mean of x mean of y
0.3628157 0.3632935
plot(crowdfunding$PovRate1,crowdfunding$successful.rate,pch=19,col=crowdfunding$Region,ylab="Successful Rate",xlab="PovRate1",main="Successful Rate-PovRate1 Plot with lowess line")
points(lowess(crowdfunding$PovRate1,crowdfunding$successful.rate,f=1/3),pch=4,col="orange",type="l")
#abline(lm(crowdfunding$successful.rate~crowdfunding$PovRate1),col="orange")
legend("bottomright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 19,col=1:4)
qqplot(crowdfunding$PovRate1,crowdfunding$successful.rate,pch=1,col=crowdfunding$Region,main="QQ plot: (Successful Rate & PovRate)")
qqline(crowdfunding$PovRate1 ,crowdfunding$successful.rate,col="red")
the condition has length > 1 and only the first element will be used
legend("topleft",legend = levels(crowdfunding$Region), pch = 19,col=1:3)
par(mfrow=c(1,1))

#qqnorm(crowdfunding$successful.rate,col=crowdfunding$Region,xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
qqnorm(crowdfunding$PovRate1,col=crowdfunding$Region,pch=18,xlab ="PovRate1")
qqline(crowdfunding$PovRate1,col="red")

Factors Analysis-Successful Rate|GiniCoeff
ggplot(crowdfunding,aes(x=GiniCoeff,y=successful.rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")
anova(successful.rate2GiniCoeff)
Analysis of Variance Table
Response: crowdfunding$successful.rate
Df Sum Sq Mean Sq F value Pr(>F)
crowdfunding$GiniCoeff 1 0.06236 0.062361 9.1537 0.003981 **
Residuals 48 0.32701 0.006813
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow=c(1,2))

boxplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,names=c("Successful rate","GiniCoeff"))
boxplot(crowdfunding$successful.rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful.rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High GiniCoeff)","Successful%(Low GiniCoeff)"),xlab="Successful rate by GiniCoeff")

t.test(crowdfunding$successful.rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful.rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)])
Welch Two Sample t-test
data: crowdfunding$successful.rate[crowdfunding$GiniCoeff > mean(crowdfunding$GiniCoeff)] and crowdfunding$successful.rate[crowdfunding$GiniCoeff <= mean(crowdfunding$GiniCoeff)]
t = 1.6383, df = 43.111, p-value = 0.1086
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.009375926 0.090607105
sample estimates:
mean of x mean of y
0.3833720 0.3427564
plot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="GiniCoeff",main="Successful Rate-GiniCoeff Plot with lowess line")
points(lowess(crowdfunding$successful.rate,crowdfunding$GiniCoeff,f=1/3),pch=4,col="red",type="l")
qqplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,pch=19,col="red",main="Q-Q Plot: Successful Rate-GiniCoeff")
qqline(crowdfunding$successful.rate,crowdfunding$GiniCoeff)
the condition has length > 1 and only the first element will be used
#qqnorm(crowdfunding$successful.rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
par(mfrow=c(1,1))

qqnorm(crowdfunding$GiniCoeff,col="blue",pch=20,xlab="GiniCoeff")
qqline(crowdfunding$GiniCoeff,col="red")

Factors Analysis-Successful Rate|Adanced Education
ggplot(crowdfunding,aes(x=pAdDeg,y=successful.rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")
anova(successful.rate2pAdDeg)
Analysis of Variance Table
Response: crowdfunding$successful.rate
Df Sum Sq Mean Sq F value Pr(>F)
crowdfunding$pAdDeg 1 0.05947 0.059469 8.6527 0.005015 **
Residuals 48 0.32990 0.006873
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow=c(1,2))

boxplot(crowdfunding$successful.rate,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$successful.rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful.rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High Adanced Education)","Successful%(Low Adanced Education)"),xlab="Successful rate by Adanced Education")

t.test(crowdfunding$successful.rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful.rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])
Welch Two Sample t-test
data: crowdfunding$successful.rate[crowdfunding$pAdDeg > mean(crowdfunding$pAdDeg)] and crowdfunding$successful.rate[crowdfunding$pAdDeg <= mean(crowdfunding$pAdDeg)]
t = 3.5483, df = 45.573, p-value = 0.0009119
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.03480093 0.12610121
sample estimates:
mean of x mean of y
0.4097258 0.3292747
plot(crowdfunding$successful.rate,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="Successful Rate-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$successful.rate,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")
qqplot(crowdfunding$successful.rate,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: Successful Rate-Adanced Education")
qqline(crowdfunding$successful.rate,crowdfunding$pAdDeg)
the condition has length > 1 and only the first element will be used
#qqnorm(crowdfunding$successful.rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
par(mfrow=c(1,1))

qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")

---
title: "Assignment"
author: "sn0wfree"
date: "12/16/2016"
output:
  html_notebook:
    toc: yes
  html_document:
    toc: yes
  pdf_document:
    toc: yes
---


## Import Data
```{r import date}
crowdfunding<-read.csv( "forqrm.csv" ,header=1)
head(crowdfunding)
library(lmtest)
rownames(crowdfunding)<-crowdfunding$State
```

## Find Topics
### 1.Geography information

1. Geography information:found the significally different by state/by region
  + Amount
  + successfull rate
  within graphy/plot
  
### 2. Factors analysis:
  
2. factors:studying the relationship between Successful Rate and other factors:
  + Higher Eduction:pAdDeg;
  + Ginicoeff
  # average_pledged_amount_of_Grand.Total#


### 3. Total regression
```{r regression}

successful.rate2GiniCoeff<-lm(crowdfunding$successful.rate~crowdfunding$GiniCoeff)#significant:0.00398
summary(successful.rate2GiniCoeff)

successful.rate2NPov000s<-lm(crowdfunding$successful.rate~crowdfunding$NPov000s)#0.231
summary(successful.rate2NPov000s)

summary(lm(crowdfunding$successful.rate~crowdfunding$pHigh))#bad:0.2320 
summary(lm(crowdfunding$successful.rate~crowdfunding$pBatDeg))#low:0.05511

successful.rate2pAdDeg<-lm(crowdfunding$successful.rate~crowdfunding$pAdDeg)#significant:0.00501
summary(successful.rate2pAdDeg)

#supplement regression
summary(lm(crowdfunding$GiniCoeff~crowdfunding$pAdDeg))#significant:0.0353


summary(lm(crowdfunding$count_of_Grand.Total~crowdfunding$Pop2010))#significant:0.0353

#summary(lm(successful.rate2PovRate1$residuals~crowdfunding$pAdDeg))#0.0003881
#summary(lm(successful.rate2PovRate1$residuals~crowdfunding$GiniCoeff))#0.0264

summary(lm(successful.rate2GiniCoeff$residuals~crowdfunding$pAdDeg))#residuals ~ ADdeg:0.0368
#summary(lm(successful.rate2GiniCoeff$residuals~crowdfunding$PovRate1))#residuals ~ PovRate1:0.771

#summary(lm(successful.rate2pAdDeg$residuals~crowdfunding$PovRate1))#0.004505
summary(lm(successful.rate2pAdDeg$residuals~crowdfunding$GiniCoeff))#0.0295


#summary(lm(log(crowdfunding$average_pledged_amount_of_Grand.Total)~crowdfunding$GiniCoeff))#0.02357


```

## Geography information



### Geography information-preprocess:kmean

```{r kmeans}
hc<-hclust(dist(crowdfunding),method = "ward.D", members = NULL)
plclust(hc)
rect.hclust(hc,k=4)


heatmap(as.matrix(dist(crowdfunding,method= 'euclidean')),labRow = F, labCol = F)
result<-cutree(hc,k=4)
result.category<-as.data.frame(result)
colnames(result.category)<-c("MDS_Category")
result.category
pie(result)
barplot(result,col =result )
#table(result)
#summary(result)
plot(result,type = "p",col=result,xlab="State",xaxt="n",ylab="MDS_Category")


library(ggplot2)
mds2 <- -cmdscale(dist(crowdfunding))
plot(mds2, type="n", axes=FALSE, ann=FALSE)
text(mds2, labels=rownames(mds2), xpd = NA)

mds<-cmdscale(dist(crowdfunding),k=4,eig=T)
x = mds$points[,1]
y = mds$points[,2]
p=ggplot(data.frame(x,y),aes(x,y))
p+geom_point(size=5 , alpha=0.8 , aes(colour=factor(result) ))
k2<-kmeans(all,centers=5,nstart=10)
summary(k2)
#-------------------------------
crowdfunding.backup<-crowdfunding

result.category["State"]<-row.names(result.category)

crowdfunding<-crowdfunding[c("State","successful.rate","Region","count_of_Grand.Total","GiniCoeff","pAdDeg","pHigh","pBatDeg","Pop2010")]
crowdfunding$Pop2010<-log(crowdfunding$Pop2010)
#crowdfunding$count_of_Grand.Total<-log(crowdfunding$count_of_Grand.Total)
crowdfunding<-merge(crowdfunding,result.category,all.x=TRUE)
row.names(crowdfunding)<-crowdfunding$State

crowdfunding$State<-NULL
crowdfunding
  
```






### Geography Information-simpleplot
```{r Geography Information-simpleplot}
par(mfrow=c(1,2) )

#count_of_Grand.Total
plot(crowdfunding$count_of_Grand.Total,col=crowdfunding$Region, main="Count of Project  Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("center",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)



#successful.rate
plot(crowdfunding$successful.rate,col=crowdfunding$Region, main="Successful Rate Plot",ylab="Successful Rate",xaxt="n",xlab="State")
#axis(side=1,at=c(1,2,3,4,5,6,7,8),labels=c(crowdfunding$State))
legend("bottomleft",legend = levels(crowdfunding$Region),cex = 0.8, pch = 1,col=1:4)
```

### Geography information-boxplot
```{r Geography information-boxplot}
par(mfrow=c(1,3))
#Boxplot for successful.rate and count_of_Grand.Total
#count_of_Grand.Total
boxplot(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="Count of Projects BoxPlot by Region")

boxplot(log(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Midwest"]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="South"]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"]),names=levels(crowdfunding$Region),main="Log Count of Projects BoxPlot by Region")

#successful.rate
boxplot(crowdfunding$successful.rate[crowdfunding$Region=="Midwest"],crowdfunding$successful.rate[crowdfunding$Region=="Northeast"],crowdfunding$successful.rate[crowdfunding$Region=="South"],crowdfunding$successful.rate[crowdfunding$Region=="West"],names=levels(crowdfunding$Region),main="Successful Rate BoxPlot by Region")
```


### Geography information-t.test
```{r Geography information-t.test}
#t.test(crowdfunding$successful.rate[crowdfunding$Region=="West"],crowdfunding$successful.rate[crowdfunding$Region=="Northeast"])
#t.test(crowdfunding$count_of_Grand.Total[crowdfunding$Region=="West"],crowdfunding$count_of_Grand.Total[crowdfunding$Region=="Northeast"])


#calcualte P Value in the t.test of Successful Rate by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-t.test(crowdfunding$successful.rate[crowdfunding$Region==location1],crowdfunding$successful.rate[crowdfunding$Region==location2])
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
SR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(SR.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Successful Rate by Region")
SR.t.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by Region 

p=NULL
temp<-NULL
for (location1 in c(levels(crowdfunding$Region))){
  for (location2 in c(levels(crowdfunding$Region))){
    if (1){
      temp<-t.test(log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location1]),log(crowdfunding$count_of_Grand.Total[crowdfunding$Region==location2]))
      if(temp$p.value<=0.1){
        #print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
CP.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(levels(crowdfunding$Region)))
colnames(CP.t.test.p.vlaue)<-c(levels(crowdfunding$Region))
print("Count of Projects by Region ")
CP.t.test.p.vlaue
#--------------------------------------------------------
#calcualte P Value in the t.test of Count of projects by kmeans 
"MDS-Category"
p=NULL

temp<-NULL
for (location1 in 1:4){
  for (location2 in 1:4){
    if (1){
      temp<-t.test(crowdfunding$successful.rate[crowdfunding$MDS_Category==location1],crowdfunding$successful.rate[crowdfunding$MDS_Category==location2])
      if(temp$p.value<=0.1){
        print(c(location1,location2,temp$p.value))
      }
      p<-c(p,temp$p.value)}}}
MDSR.t.test.p.vlaue<-as.data.frame(matrix(p,4,4),row.names = c(1:4))
colnames(MDSR.t.test.p.vlaue)<-c(1:4)
print("Successful Rate by Region")
MDSR.t.test.p.vlaue


```

## Factors analysis
This article is to analyse the factors to the crowdfunding successful rate.
I guess the Education, the inequity of family income and the poverty rate may be related to the crowdfunding successful rate. and in the follow context, i will analyse the those factors.

Firstly, The Statistical Summary
### Factors Analysis-Statistical Summary
```{r Factors Analysis-Statistical Summary}
library(moments)

summary(crowdfunding$successful.rate)
kurtosis(crowdfunding$successful.rate)

summary(crowdfunding$GiniCoeff)
kurtosis(crowdfunding$GiniCoeff)

summary(crowdfunding$pAdDeg)
kurtosis(crowdfunding$pAdDeg)

summary(crowdfunding$PovRate1)
kurtosis(crowdfunding$PovRate1)

```

### Factors Analysis-Plot for Factors
```{r Plot for Factors}

boxplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,crowdfunding$pAdDeg,crowdfunding$PovRate1,names = c("Successful Rate","GiniCoeff","Higher Education","PovRate1"),main="Factors Box Plot")

par(mfrow=c(1,3))
plot(crowdfunding$successful.rate,col="red",pch=10,xlab="State",ylab="Successful Rate",xaxt="n",main="Successful Rate Plot")
plot(crowdfunding$GiniCoeff,col="green",pch=18,xlab="State",xaxt="n",ylab="GiniCoeff ",xaxt="n",main="GiniCoeff Plot")
plot(crowdfunding$pAdDeg,col="blue",pch=15,xlab="State",xaxt="n",ylab="Adanced Education Rate",xaxt="n",main="Adanced Education Rate Plot")
#plot(crowdfunding$PovRate1,col="black",pch=16,xlab="State",xaxt="n",ylab="Poverty Rate",xaxt="n",main="Poverty Rate Plot")

```





```{r scatterplot}
library(car)
scatterplot(crowdfunding$successful.rate,log(crowdfunding$average_of_goal_Grand.Total),pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$GiniCoeff,pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$PovRate1,pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$Densitym2,pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$pHigh,pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$pBatDeg,pch=19)
scatterplot(crowdfunding$successful.rate~crowdfunding$pAdDeg,pch=19)


```


### Factors Analysis-Successful Rate|PovRate1
```{r Successful Rate-PovRate1}
#redo scatterplot with Successful Rate-PovRate1
scatterplot(crowdfunding$successful.rate,crowdfunding$PovRate1,pch=19)

anova(successful.rate2PovRate1)

ggplot(crowdfunding,aes(x=PovRate1,y=successful.rate,main = "Successful rate~PovRate"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")

par(mfrow=c(1,2))

boxplot(crowdfunding$successful.rate,crowdfunding$PovRate1,names=c("Successful Rate","PovRate1"))
boxplot(crowdfunding$successful.rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful.rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)],col = c("green","deepskyblue"),names=c("Successful%(High PovRate)","Successful%(Low PovRate)"),xlab="Successful rate by PovRate1")



t.test(crowdfunding$successful.rate[crowdfunding$PovRate1>mean(crowdfunding$PovRate1)],crowdfunding$successful.rate[crowdfunding$PovRate1<=mean(crowdfunding$PovRate1)])



plot(crowdfunding$PovRate1,crowdfunding$successful.rate,pch=19,col=crowdfunding$Region,ylab="Successful Rate",xlab="PovRate1",main="Successful Rate-PovRate1 Plot with lowess line")
points(lowess(crowdfunding$PovRate1,crowdfunding$successful.rate,f=1/3),pch=4,col="orange",type="l")
#abline(lm(crowdfunding$successful.rate~crowdfunding$PovRate1),col="orange")
legend("bottomright",legend = levels(crowdfunding$Region),cex = 0.8, pch = 19,col=1:4)


qqplot(crowdfunding$PovRate1,crowdfunding$successful.rate,pch=1,col=crowdfunding$Region,main="QQ plot: (Successful Rate & PovRate)")
qqline(crowdfunding$PovRate1 ,crowdfunding$successful.rate,col="red")
legend("topleft",legend = levels(crowdfunding$Region), pch = 19,col=1:3)




par(mfrow=c(1,1))
#qqnorm(crowdfunding$successful.rate,col=crowdfunding$Region,xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
qqnorm(crowdfunding$PovRate1,col=crowdfunding$Region,pch=18,xlab ="PovRate1")
qqline(crowdfunding$PovRate1,col="red")
```





### Factors Analysis-Successful Rate|GiniCoeff
```{r Successful Rate-GiniCoeff}

ggplot(crowdfunding,aes(x=GiniCoeff,y=successful.rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")

anova(successful.rate2GiniCoeff)



par(mfrow=c(1,2))


boxplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,names=c("Successful rate","GiniCoeff"))
boxplot(crowdfunding$successful.rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful.rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High GiniCoeff)","Successful%(Low GiniCoeff)"),xlab="Successful rate by GiniCoeff")





t.test(crowdfunding$successful.rate[crowdfunding$GiniCoeff>mean(crowdfunding$GiniCoeff)],crowdfunding$successful.rate[crowdfunding$GiniCoeff<=mean(crowdfunding$GiniCoeff)])


plot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="GiniCoeff",main="Successful Rate-GiniCoeff Plot with lowess line")
points(lowess(crowdfunding$successful.rate,crowdfunding$GiniCoeff,f=1/3),pch=4,col="red",type="l")


qqplot(crowdfunding$successful.rate,crowdfunding$GiniCoeff,pch=19,col="red",main="Q-Q Plot: Successful Rate-GiniCoeff")
qqline(crowdfunding$successful.rate,crowdfunding$GiniCoeff)


#qqnorm(crowdfunding$successful.rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$GiniCoeff,col="blue",pch=20,xlab="GiniCoeff")
qqline(crowdfunding$GiniCoeff,col="red")

```



### Factors Analysis-Successful Rate|Adanced Education
```{r Successful Rate-Adanced Education}
ggplot(crowdfunding,aes(x=pAdDeg,y=successful.rate,main = "Successful rate~GiniCoeff"))+geom_point(aes(col=Region))+geom_smooth(method = "loess")

anova(successful.rate2pAdDeg)

par(mfrow=c(1,2))

boxplot(crowdfunding$successful.rate,crowdfunding$pAdDeg,names=c("Successful rate","Adanced Education"))
boxplot(crowdfunding$successful.rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful.rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)],col = c("darkorchid2","dodgerblue"),names=c("Successful%(High Adanced Education)","Successful%(Low Adanced Education)"),xlab="Successful rate by Adanced Education")


t.test(crowdfunding$successful.rate[crowdfunding$pAdDeg>mean(crowdfunding$pAdDeg)],crowdfunding$successful.rate[crowdfunding$pAdDeg<=mean(crowdfunding$pAdDeg)])


plot(crowdfunding$successful.rate,crowdfunding$pAdDeg,f=1/3 ,pch=19,col="blue",xlab="Successful Rate",ylab="Adanced Education",main="Successful Rate-Adanced Education Plot with lowess line")
points(lowess(crowdfunding$successful.rate,crowdfunding$pAdDeg,f=1/3),pch=4,col="red",type="l")


qqplot(crowdfunding$successful.rate,crowdfunding$pAdDeg,pch=19,col="red",main="Q-Q Plot: Successful Rate-Adanced Education")
qqline(crowdfunding$successful.rate,crowdfunding$pAdDeg)


#qqnorm(crowdfunding$successful.rate,col="orange",xlab="Successful Rate")
#qqline(crowdfunding$successful.rate,col="red")
par(mfrow=c(1,1))
qqnorm(crowdfunding$pAdDeg,col="blue",pch=20,xlab="Adanced Education")
qqline(crowdfunding$pAdDeg,col="red")

```

